Root Mean Square Error Of Prediction Research Articles

FT-MIR-ATR Associated with Chemometrics Methods: A Preliminary Analysis of Deterioration State of Brazil Nut Oil.

Brazil nut oil is highly valued in the food, cosmetic, chemical, and pharmaceutical industries, as well as other sectors of the economy. This work aims to use the Fourier transform infrared (FTIR) technique associated with partial least squares regression (PLSR) and principal component analysis (PCA) to demonstrate that these methods can be used in a prior and rapid analysis in quality control. Natural oils were extracted and stored for chemical analysis. PCA presented two groups regarding the state of degradation, subdivided into super-degraded and partially degraded groups in 99.88% of the explained variance. The applied PLS reported an acidity index (AI) prediction model with root mean square error of calibration (RMSEC) = 1.8564, root mean square error of cross-validation (REMSECV) = 4.2641, root mean square error of prediction (RMSEP) = 2.1491, R2cal (calibration correlation coefficient) equal to 0.9679, R2val (validation correlation coefficient) equal to 0.8474, and R2pred (prediction correlation coefficient) equal to 0, 8468. The peroxide index (PI) prediction model showed RMSEC = 0.0005, REMSECV = 0.0016, RMSEP = 0.00079, calibration R2 equal to 0.9670, cross-validation R2 equal to 0.7149, and R2 of prediction equal to 0.9099. The physical-chemical analyses identified that five samples fit in the food sector and the others fit in other sectors of the economy. In this way, the preliminary monitoring of the state of degradation was reported, and the prediction models of the peroxide and acidity indexes in Brazil nut oil for quality control were determined.

Comparison of Raman and mid-infrared spectroscopy for quantification of nitric acid in PUREX-relevant mixtures

During the plutonium uranium reduction extraction (PUREX) process, nitric acid facilitates the extraction of actinides from the aqueous phase into the organic phase by forming neutral, organic soluble complexes with tri-n-butyl phosphate (TBP). The concentration of nitric acid is generally measured by titration; however, titration is a time-consuming method that generates significant volumes of additional waste. Optical spectroscopic techniques can be used to perform fast, automated measurements off-line or on-line, without generating any waste. In this work, the effectiveness of Raman and mid-infrared (MIR) spectroscopy has been compared for the first time as an alternative to titration for the quantification of nitric acid in PUREX-relevant mixtures. Samples of 0–12 M nitric acid in the aqueous phase and 0–1.10 M nitric acid in the organic phase (TBP/odourless kerosene (OK)–H2O–HNO3 model system) were analysed and partial least squares (PLS) regression models were built to predict nitric acid concentration. MIR spectra required less pre-processing than Raman spectra and more accurate predictions of nitric acid concentration were obtained for MIR spectroscopy than for Raman spectroscopy, with root mean square error of prediction (RMSEP) values of 0.099 M versus 0.148 M obtained for the aqueous phase and root mean square error of cross validation (RMSECV) values of 0.006 M versus 0.013 M obtained for the organic phase. To investigate the ability to predict nitric acid concentration in the presence of uranyl nitrate, samples containing uranium (0–100 g/L) and nitric acid (0.15–0.64 M) in the organic phase (U-TBP/OK-H2O–HNO3 model system) were analysed by Raman and MIR spectroscopy. The RMSECV was 0.027 M and 0.066 M for MIR and Raman spectroscopy, respectively; these values are higher than those obtained in the absence of uranyl nitrate owing to differences in the experimental approaches employed. Therefore, the results obtained demonstrate that MIR or Raman spectroscopy could be used to measure the concentration of nitric acid in the organic and aqueous phases in the PUREX process.

Classification and quantification of multiple adulterants simultaneously in black tea using spectral data coupled with chemometric analysis

Black tea is a popularly consumed beverage across the world. However, the adulteration of tea powder with extraneous colors is causing serious health threats. In the present study four such color adulterants viz., tartrazine, sunset yellow, carmoisine and ponceau 4R were simultaneously detected using FT-IR spectral data coupled with chemometric tools. PLS-DA (partial least squares discriminant analysis) was used for the classification of adulterants, whereas, PLS2 (multi response PLS) and LS-SVM (least-squares support vector machines) were used for quantification purpose. RCGA (real coded genetic algorithm) was used as a feature selection algorithm to obtain fewer fingerprints from the FT-IR spectrum of the adulterated tea powders. PLS-DA was able to predict with 100% accuracy for the samples spiked with all four adulterants when only 30 fingerprints for each adulterant were used. Amongst the regression algorithms, LS-SVM was observed to be superior over PLS2 having lower root-mean-square error of prediction (RMSEP) for carmoisine and ponceau 4R when randomized statistical test was conducted. The prediction results had high regression coefficient (R2p >0.89) with REP values ranging between 10.90% and 18.40%, and RPD values in the range of 2.82–5.81, for simultaneous quantification four adulterants using 30 variables LS-SVM model. To study matrix effect, an additional experiment was carried to observe the effectiveness of above established methodologies. Towards this, six different indigenous commercial tea brands were selected and the robustness of these chemometric methods were demonstrated. This study provides a robust tool for infrared based simultaneous detection and quantification of four different color adulterants in black tea powders.

The Analysis and Rapid Non-Destructive Evaluation of Yongchuan Xiuya Quality Based on NIRS Combined with Machine Learning Methods

This paper attempts to analyze and assess Yongchuan Xiuya tea quality quickly, accurately, and digitally. The sensory evaluation method was first used to assess Yongchuan Xiuya tea quality, and then near infrared spectroscopy (NIRS) was obtained, and standard methods were applied to the testing of the chemical components. Next, principal component analysis (PCA) and the correlation coefficient method were used to comprehensively screen out the representative components. Finally, NIRS combined with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN) methods were applied to build quality evaluation models for Yongchuan Xiuya tea, respectively, and external samples were employed to examine the practical application results of the best model. The cumulative variance contribution rate of the first three principal components of the ingredients in tea was 97.73%. Seven components closely related to tea quality were screened out, namely, amino acids, total catechin, epigallocatechin gallate (EGCG), tea polyphenols, water extracts, epicatechin gallate (ECG), and epigallocatechin (EGC) (p < 0.01). Between the two models established to predict the tea quality, the model built by the PLS method had the better results, whose coefficient of determination of prediction (Rp2) and root mean square error of prediction (RMSEP) were 0.7955 and 1.2263, respectively, and the best results were obtained by the nonlinear BP-ANN model, whose Rp2 and RMSEP were 0.9315 and 0.6787, respectively. The 10 external Yongchuan Xiuya samples were employed to test the best BP-ANN model, and the results of R2 and RMSEP were 0.9579 and 0.6086, respectively, meaning that the model has good robustness. Therefore, the model established by NIRS combined with the BP-ANN method can be used to assess Yongchuan Xiuya tea quality rapidly, accurately, and digitally, and it can also provide new ideas and methods for evaluating the quality of other teas.

Turning Zr(IV) into a phosphate ester mimetic enzyme via de novo synthesis for hydrolyzing organophosphorus warfare agents and rapid activity prediction by near-infrared spectroscopy

Chemical warfare agents, especially organophosphorus warfare agents are a variety of various chemical substances employed in warfare that can massively cause damage to the organism even death. The partial catalytic function of Universitetet i Oslo-66 (UiO-66, a zirconium-based metal–organic framework) produced via de novo synthesis is similar to calf intestinal alkaline phosphatase (CIAP, EC 3.1.3.1) and has a lower usage cost, is expected to be a mimetic enzyme for hydrolyzing organophosphorus warfare agents due to the fact that it has lattice defects. 0.1–5.6 mL of acetic acid was added as a regulator to form different lattice defects. Typically, mimetic enzyme activity tests often require expensive reagents as substrates, and the experiment processes are time-consuming and inconvenient. As a result, a novel approach based on near-infrared spectroscopy (NIRs) was developed in this research for predicting UiO-66 mimetic enzyme activity. p-Nitrophenyl phosphate disodium salt (pNPP) was selected as a lower toxic organophosphorus warfare agent to conduct the subsequent simulant research. The absorbance of the mimetic enzyme catalytic system was evaluated using Ultraviolet–Visible (UV–Vis) spectroscopy to investigate the amount of its activity and compare it with CIAP activity. The synthesized samples’ enzyme activities ranged from 1.91 to 6.67 U/g and their catalytic costs ranged from 0.65 to 2.26 CNY. The powdered samples' near-infrared spectra were obtained. The sample’s enzyme activity data were linked with each sample’s original NIR spectra to establish the NIR model. A partial least squares (PLS) model of mimetic enzyme activity was developed by coupling spectral preprocessing with a variable screening technique. The root-mean-square error of cross-validation (RMSECV), the correlation coefficient of validation set (R) value, and the ratio of prediction to deviation (RPD) value were employed as assessment model indicators. The near-infrared spectrum model was developed by merging the best 2nd derivative spectral preprocessing with the Competitive Adaptive Reweighted Sampling (CARS) variable screening method. This model's RMSECV was 0.41 U/g, the correlation coefficient of calibration set (R_cv) value was 0.916, the root-mean-square error of prediction (RMSEP) set was 0.52 U/g, R was 0.948, and RPD was 2.51. As a result, the established model could be implemented to quantify the activity of UiO-66 quickly by including more variations of de novo-synthesized samples. The prediction method is simple, rapid, low cost and adaptable to be the theoretical and practical basis for further studying other interdisciplinary research work in biomimetic enzymology and spectroscopy.

Determination of basic nitrogen content in diesel oil: A miniaturized method by digital image-based colorimetry in a portable device

A miniaturized method using a portable device with digital image acquisition and PhotoMetrix PRO app data treatment was developed for the determination of basic nitrogen content in diesel oil. The method was based on the colorimetric titration described in the UOP 269-10 standard protocol. A homemade 3D-printed chamber with controlled light intensity equipped with an USB camera was used for image acquisition after an acid-base titration reaction, carried out in a miniaturized device. After mixing reagents and diesel oil, the images were obtained and converted into RGB (red, green, and blue) histograms, and a partial least squares (PLS) multivariate calibration model was constructed. Parameters of the regression model were evaluated, by the coefficient of determination (R2), the root mean squared error of calibration (RMSEC), the root mean squared error of cross-validation (RMSECV), and the root mean squared error of prediction (RMSEP). Some conditions for the acid-base titration were optimized, such as the concentration of the indicator (68.0 to 272 µmol L-1) and the titrating (HClO4, 0.179 to 1.79 mmol L-1), as well as the volume of diesel oil. With 60 µL of 2.54 mmol L-1 indicator solution, 20 µL of 20 mmol L-1 HClO4 as titrating and using 50 to 1000 µL of diesel oil, optimal conditions were obtained for calibration (RMSEP of 0.377 mg kg-1, RMSECV of 0.307 mg kg-1 with 4 factors). It is important to mention that no differences were observed (p < 0.05) when comparing reference values with the results by the proposed protocol. This proved to be advantageous in relation to the methods described in the UOP 269-10 standard since it was possible to reduce the consumption of reagents and waste generation, in agreement with green analytical chemistry. In addition, this alternative protocol combines simplicity and speed to obtain results with good accuracy, precision and suitable limit of quantification (1 mg kg-1) using a miniaturized system.

Authentication analysis of milkfish oil treated with activated charcoal and bentonite using Fourier Transform Infrared Spectroscopy and Multivariate Data Analysis

Adulteration issues are important not only for fats and oils consumers but also for producers and regulatory bodies, therefore authentication analytical methods are continuously developed. The objective of this study was to highlight the employment of attenuated total Fourier transform infrared (FTIR) spectroscopy and chemometrics for the authentication of milkfish oil (MFO) from pumpkin seed oil (PSO). In this study, ATRFTIR spectra of MFO, PSO and its binary mixtures were scanned, and the information obtained was used for quantitative analysis of adulterants and classification between authentic MFO and MFO adulterated with PSO. Two multivariate calibrations of partial least square calibration (PLSR) and principal component regression (PCR) were compared for their performance during quantitative analysis and linear discriminant analysis was applied for discrimination between authentic MFO and MFO adulterated with PSO. The results revealed that PLSR using 2nd derivative spectra at wavenumbers of 1500-1000 cm-1 was preferred for simultaneous quantitative analysis of MFO-treated with activated charcoal, while PCR using 2nd derivative spectra at wavenumbers of 1500-1000 cm-1 was suitable for analysis of MFO-treated with bentonite providing the acceptable values of R2 , root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). Linear discriminant Analysis using normal FTIR spectra at the whole wavenumber region was successful for the discrimination of MFO treated with activated charcoal and bentonite with accuracy levels of 100%. It can be concluded that the chemometrics approach for ATR-FTIR spectra could be a simple and effective tool for the authentication of MFO.

Ability of three dairy feed evaluation systems to predict postruminal outflows of nitrogenous compounds in dairy cows: A meta-analysis

Adequate prediction of postruminal outflow of protein fractions is the starting point for the determination of metabolizable protein supply in dairy cows. The objective of this meta-analysis was to compare the performance of 3 dairy feed evaluation systems (National Research Council [NRC], Cornell Net Protein and Carbohydrate System [CNCPS], and National Academies of Sciences, Engineering and Medicine [NASEM]) to predict outflows (g/d) of nonammonia nitrogren (NAN), microbial N (MiN), and nonammonia nonmicrobial N (NANMN). Predictions of rumen degradabilities (% of nutrient) of protein (RDP), NDF, and starch were also evaluated. The data set included 1,294 treatment means from 312 digesta flow studies. The 3 feed evaluation systems were compared using the concordance correlation coefficient (CCC), the ratio of root mean square prediction error (RMSPE) on standard deviation of observed values (RSR), and the slope between observed and predicted values. Mean and linear biases were deemed biologically relevant and are discussed if higher than a threshold of 5% of the mean of observed values. The comparisons were done on observed values adjusted or not for the study effect; the adjustment had a small effect on the mean bias but the linear bias reflected a response to a dietary change rather than absolute predictions. For the absolute predictions of NAN and MiN, CNCPS had the best-fit statistics (8% greater CCC; 6% lower RMSPE) without any bias; NRC and NASEM underpredicted NAN and MiN, and NASEM had an additional linear bias indicating that the underprediction of MiN increased at increased predictions. For NANMN, fit statistics were similar among the 3 feed evaluation systems with no mean bias; however, the linear bias with NRC and CNCPS indicated underprediction at low predictions and overprediction at elevated predictions. On average, the CCC were smaller and RSR ratios were greater for MiN versus NAN indicating increased prediction errors for MiN. For NAN responses to a dietary change, CNCPS also had the best predictions, although the mean bias with NASEM was not biologically relevant and the 3 feed evaluation systems did not present a linear bias. However, CNCPS, but not the 2 other feed evaluation systems, presented a linear bias for MiN, with responses being overpredicted at increased predictions. For NANMN, responses were overpredicted at increased predictions for the 3 feed evaluation systems, but to a lesser extent with NASEM. The site of sampling had an effect on the mean bias of MiN and NANMN in the 3 feed evaluation systems. The mean bias of MiN was higher in omasal than duodenal studies in the 3 feed evaluation systems (from 55 to 61 g/d) and this mean bias was twice as large when 15N labeling was used as a microbial marker compared with purines. Such a difference was not observed for duodenal studies. The reasons underlying these systematic differences are not clear as the type of measurements used in the current meta-analysis does not allow to delineate if one site or one microbial marker is yielding the "true" postruminal N outflows. Rumen degradabilities of protein was underpredicted with CNCPS, and RDP responses to a dietary change was underpredicted by the 3 feed evaluation systems with increased RDP predictions. Rumen degradability of NDF was underpredicted and had poor fit statistics for NASEM compared with CNCPS. Fit statistics were similar between CNCPS and NASEM for rumen degradability of starch, but with an underprediction of the response with NASEM and absolute values being overpredicted with CNCPS. Multivariate regression analyses showed that diet characteristics were correlated with prediction errors of N outflows in each feed evaluation system. Globally, compared with NAN and NANMN, residuals of MiN were correlated with several moderators in the 3 feed evaluation systems reflecting the complexity to measure and model this outflow. In addition, residuals of NANMN were correlated positively with RDP suggesting an overestimation of this parameter. In conclusion, although progress is still to be made to improve equations predicting postruminal N outflows, the current feed evaluation systems provide sufficient precision and accuracy to predict postruminal outflows of N fractions.

Raman spectroscopy applied to online monitoring of a bioreactor: Tackling the limit of detection

An in-situ monitoring model of alcoholic fermentation based on Raman spectroscopy was developed in this study. The optimized acquisition parameters were an 80 s exposure time with three accumulations. Standard solutions were prepared and used to populate a learning database. Two groups of mixed solutions were prepared for a validation database to simulate fermentation at different conditions. First, all spectra of the standards were evaluated by principal component analysis (PCA) to identify the spectral features of the target substances and observe their distribution and outliers. Second, three multivariate calibration models for prediction were developed using the partial least squares (PLS) method, either on the whole learning database or subsets. The limit of detection (LOD) of each model was estimated by using the root mean square error of cross validation (RMSECV), and the prediction ability was further tested with both validation datasets. As a result, improved LODs were obtained: 0.42 and 1.55 g·L−1 for ethanol and glucose using a sub-learning dataset with a concentration range of 0.5 to 10 g·L−1. An interesting prediction result was obtained from a cross-mixed validation set, which had a root mean square error of prediction (RMSEP) for ethanol and glucose of only 3.21 and 1.69, even with large differences in mixture concentrations. This result not only indicates that a model based on standard solutions can predict the concentration of a mixed solution in a complex matrix but also offers good prospects for applying the model in real bioreactors.

Tea authentication and determination of chemical constituents using digital image-based fingerprint signatures and chemometrics.

Tea (Camellia sinensis) fraud has been frequently identified and involves tampering with the labelling of inferior products or without geographical origin certification and even mixing them with superior quality teas to mask an adulteration. Consequently, economic losses and health damage to consumers are observed. Thus, a Chemometrics-assisted Color Histogram-based Analytical System (CACHAS) was employed a simple, cost-effective, reliable, and green analytical tool to screen the quality of teas. Data-Driven Soft Independent Modeling of Class Analogy was used to authenticate their geographical origin and category simultaneously, recognizing correctly all Argentinean and Sri Lankan black teas and Argentinean green teas. For the determination of moisture, total polyphenols, and caffeine, Partial Least Squares obtained satisfactory predictive abilities, with values of root mean squared error of prediction (RMSEP) of 0.50, 0.788, and 0.25mgkg-1, rpred of 0.81, 0.902, and 0.81, and relative error of prediction (REP) of 6.38, 9.031, and 14.58%., respectively. CACHAS proved to be a good alternative tool for environmentally-friendly non-destructive chemical analysis.

The splenic evanescent trauma identification and the injured time estimation in decomposing cadavers based on ATR-FTIR and chemometrics

Trauma is one of the most prevalent disorders in the biomedical field. Abdominal trauma is widely encountered in accidents and somecriminal situations, and it has a significant influence on the investigation or conviction of violent crimes. With about 33% of cases involving blunt abdominal trauma, the spleen is the organ that suffers the most and is associated with a significant risk of morbidity and mortality, particularly in patients who have accumulated multiple or compound injuries. “Evanescent trauma” describes an injury that becomes invisible as time passes, decomposition occurs, and other factors involved.In this study, we have found methods to identify splenic evanescent trauma and estimate the injured time in postmortem cadavers by FTIR combination with chemometrics. The PLSDA model to identify splenic evanescent trauma in decomposing cadavers' specificity of externally validation was 0.917, sensitivity was 0.958, and accuracy was 0.950. The PLSDA model to identify DSR and SR in decomposing cadavers' specificity was 0.833, sensitivity was 0.889, and accuracy was 0.875.To eliminate the impact cause by the cadaver's autolysis and putrefaction, and the redundancy of full-spectral data modeling, we performed variables selection with the methods of CARS, GA, SPA. The performance of CARS-PLS was superior to GA-PLS. The SPA-CARS-PLS regression model for the injured time estimation in decomposing cadavers at PMI 2d and 10d had the better performance and better interpretability of spectral. The SPA-CARS-PLS regression model to estimate the injured time at PMI 2d was constructed with 6 latent variables, the R2CV was 0.911 and the RMSECV was 1.477 d. For external validation, the R2Pred value was 0.884. The root mean square error of prediction(RMSEP) was 2.404 d. The SPA-CARS-PLS regression model to estimate the injured time at PMI 10d was built with 6 latent variables, the R2CV was 0.858 and the RMSECV was 1.868 d. For external validation, the prediction model's R2Pred value was 0.930, the RMSEP was 1.487 d. FTIR combine with chemometrics is useful in identifying the splenic evanescent trauma and estimating the injured time in postmortem decomposed cadavers.

Near real-time predictions of renewable electricity production at substation level via domain adaptation zero-shot learning in sequence

With the urgency of the transition to a resilient low-carbon economy, the monitoring and prediction of regional renewable energy generation over time have become increasingly important. The difficulties of renewable energy data transfer between multiple stakeholders have also caused elevated the need for more trustworthy data analytics within the energy grid. However, only a few voltage transformation facilities in the grid (i.e. substations) contain complete information about the renewable energy generated within the region. A large number of incomplete-information substations with fully-missing renewable energy data limits the analysis and policy-making related to renewable energy. This work studies the potential to transfer information from perfect-information substations to incomplete-information substations with fully-missing renewable energy data. To preserve the practicality of renewable energy prediction, a domain adaptation for zero-shot learning in sequence (DAZLS) strategy is proposed for fully-missing renewable energy prediction in substations. DAZLS is a model agnostic technique which can utilize any base model within its framework for the prediction of renewable energy generation. Using total measured power and weather information (solar irradiation and wind speed) in information-complete substations (8831 timestamps for 10 substations) within the Netherlands, we developed a model to predict solar and wind power from energy producers associated with information incomplete substations via additional real-time weather data, metadata information (e.g. geospatial position, existence and capacity of renewable facilities). Using DAZLS, the average root-mean-squared error for prediction (RMSEP) is 0.07, while that of a default transfer learning model is 0.70. This meant that renewable energy sources in information-incomplete substations could be reliably monitored using weather data, meta-data and physical data, resulting in lesser investment in power meters. This approach was demonstrated for the highest frequency prediction possible in the grid, with a near-real-time frequency of 15 min. Our method can be effectively used for renewable energy grid optimization, planning and analysis.

Performance of a Handheld MicroNIR Instrument for Determining Protein Levels in Sorghum Grain Samples.

Near infrared (NIR) spectroscopy is widely used for evaluating quality traits of cereal grains. For evaluating protein content of intact sorghum grains, parallel NIR calibrations were developed using an established benchtop instrumentation (Perten DA-7250) as a baseline to test the efficacy of an adaptive handheld instrument (VIAVI MicroNIR OnSite-W). Spectra were collected from 59 grain samples using both instruments at the same time. Cross-validated calibration models were validated with 33 test samples. The selected calibration model for DA-7250 with a coefficient of determination (R2) = 0.98 and a root mean square error of cross validation (RMSECV) = 0.41% predicted the protein content of a test set with R2 = 0.94, root mean square error of prediction (RMSEP) = 0.52% with a ratio of performance to deviation (RPD) of 4.13. The selected model for the MicroNIR with R2 = 0.95 and RMSECV = 0.62% predicted the protein content of the test set with R2 = 0.87, RMSEP = 0.76% with an RPD of 2.74. In comparison, the performance of the DA-7250 was better than the MicroNIR, however, the performance of the MicroNIR was also acceptable for screening intact sorghum grain protein levels. Therefore, the MicroNIR instrument may be used as a potential tool for screening sorghum samples where benchtop instruments are not appropriate such as for screening samples in the field or as a less expensive option compared with benchtop instruments.

Assessment of bioactive compounds in faba bean using infrared spectroscopy

AbstractFaba bean (Vicia faba) is growing in popularity in Australia, partly due to its higher levels of health‐benefiting compounds compared to other grain crops. This study investigated infrared spectroscopy for predicting levels of bioactive compounds such as antioxidants and phenolics in faba bean flour. Calibration models were performed on 60 samples of faba bean, comprising 10 varieties grown across two field locations in 1 year. For model validation, an independent test set comprising the same varieties grown in a different year was utilised. Near‐infrared spectroscopy (NIRS) showed promise for the prediction of total phenolic content, with an R2pred of 0.66 and root mean square error of prediction (RMSEP) of 76 mg/100 g. Similarly, prediction of ferric reducing antioxidant power, a measure of antioxidant activity, gave an R2pred of 0.59 and RMSEP of 87 mg/100 g. Additionally, moving window optimisation was used to determine the most important wavelength region for the prediction of these analytes. Fourier transform infrared spectroscopy did not yield any suitable models for the analytes investigated. Although the NIRS models developed were not capable of exactly quantifying phenolic or antioxidant content, infrared spectroscopy appears useful for rapidly discriminating between samples containing high and low levels of phenolics or antioxidant compounds. With further refinement, this technique could potentially be applied for the quality assurance of phenolic content or antioxidant capacity in faba bean seeds.

Statistical comparison of predictive models for quantitative analysis and classification in the framework of LIBS spectroscopy: A tutorial

Laser-Induced Breakdown Spectroscopy (LIBS) is a widely accepted technique used for both classification and quantification purposes considering complex and heterogeous samples. Based on a set of training spectra acquired from diverse and representative samples within a specific application domain, it becomes possible to apply various data processing techniques and modeling methods to construct the predictive model in question. Naturally the complexity of both the laser-matter and the laser-plasma interactions and the heterogeneity of natural samples often requires the development of various predictive models, which are then compared based on figures of merit such as the RMSEP (Root Mean Square Error of Prediction) value for quantification or the classification rate for qualitative analysis. Our ultimate goal is, of course, to select the model that appears to be the most accurate, which ultimately boils down to searching for the lowest RMSEP value or the highest classification rate. This is precisely where the whole problem lies because even if we observe a different level of error for two models, for example, this difference is not necessarily statistically significant. In such a case, we are therefore not allowed to say that the lower error indicates the best predictive model to consider. The purpose of this article is to provide a tutorial on introducing a statistical model comparison procedure, whether they are quantitative or qualitative. Two LIBS data sets have been used to illustrate the principles of the proposed method.

Non-Targeted Detection and Quantification of Food Adulteration of High-Quality Stingless Bee Honey (SBH) via a Portable LED-Based Fluorescence Spectroscopy.

Stingless bee honey (SBH) is rich in phenolic compounds and available in limited quantities. Authentication of SBH is important to protect SBH from adulteration and retain the reputation and sustainability of SBH production. In this research, we use portable LED-based fluorescence spectroscopy to generate and measure the fluorescence intensity of pure SBH and adulterated samples. The spectrometer is equipped with four UV-LED lamps (peaking at 365 nm) as an excitation source. Heterotrigona itama, a popular SBH, was used as a sample. 100 samples of pure SBH and 240 samples of adulterated SBH (levels of adulteration ranging from 10 to 60%) were prepared. Fluorescence spectral acquisition was measured for both the pure and adulterated SBH samples. Principal component analysis (PCA) demonstrated that a clear separation between the pure and adulterated SBH samples could be established from the first two principal components (PCs). A supervised classification based on soft independent modeling of class analogy (SIMCA) achieved an excellent classification result with 100% accuracy, sensitivity, specificity, and precision. Principal component regression (PCR) was superior to partial least squares regression (PLSR) and multiple linear regression (MLR) methods, with a coefficient of determination in prediction (R2p) = 0.9627, root mean squared error of prediction (RMSEP) = 4.1579%, ratio prediction to deviation (RPD) = 5.36, and range error ratio (RER) = 14.81. The LOD and LOQ obtained were higher compared to several previous studies. However, most predicted samples were very close to the regression line, which indicates that the developed PLSR, PCR, and MLR models could be used to detect HFCS adulteration of pure SBH samples. These results showed the proposed portable LED-based fluorescence spectroscopy has a high potential to detect and quantify food adulteration in SBH, with the additional advantages of being an accurate, affordable, and fast measurement with minimum sample preparation.

Comparison of Si-GA-PLS and Si-CARS-PLS build algorithms for quantitation of total polyphenols in black tea using the spectral analytical system.

The objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si-PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables. The collected spectral data were divided into 19 sub-interval selections totaling 246 variables, yielding the lowest root mean square error of cross-validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (RC ), prediction (RP ), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si-GA-PLS model produced the following results: PCs = 9; RC = 0.915; RMSECV = 1.39; RP = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si-CARS-PLS model was noted to be best at PCs = 10, while RC = 0.9723, RMSECV = 0.81, RP = 0.9114, RMSEP = 1.45 and RPD = 2.59. The build model's prediction ability was amended in the order PLS < Si-PLS < CARS-PLS when full spectroscopic data were used and Si-PLS < Si-GA-PLS < Si-CARS-PLS when interval selection was performed with the Si-PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry.

Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics.

In the process of storage and cold chain logistics, apples are prone to physical bumps or microbial infection, which easily leads to spoilage in the micro-environment, resulting in widespread infection and serious post-harvest economic losses. Thus, development of methods for monitoring apple spoilage and providing early warning of spoilage has become the focus for post-harvest loss reduction. Thus, in this study, a spoilage monitoring and early warning system was developed by measuring volatile component production during apple spoilage combined with chemometric analysis. An apple spoilage monitoring prototype was designed to include a gas monitoring array capable of measuring volatile organic compounds, such as CO2, O2 and C2H4, integrated with the temperature and humidity sensor. The sensor information from a simulated apple warehouse was obtained by the prototype, and a multi-factor fusion early warning model of apple spoilage was established based on various modeling methods. Simulated annealing-partial least squares (SA-PLS) was the optimal model with the correlation coefficient of prediction set (Rp) and root mean square error of prediction (RMSEP) of 0.936 and 0.828, respectively. The real-time evaluation of the spoilage was successfully obtained by loading an optimal monitoring and warning model into the microcontroller. An apple remote monitoring and early warning platform was built to visualize the apple warehouse's sensors data and spoilage level. The results demonstrated that the prototype based on characteristic gas sensor array could effectively monitor and warn apple spoilage.

Protein and lipid content estimation in soybeans using Raman hyperspectral imaging.

Unlike standard chemical analysis methods involving time-consuming, labor-intensive, and invasive pretreatment procedures, Raman hyperspectral imaging (HSI) can rapidly and non-destructively detect components without professional supervision. Generally, the Kjeldahl methods and Soxhlet extraction are used to chemically determine the protein and lipid content of soybeans. This study is aimed at developing a high-performance model for estimating soybean protein and lipid content using a non-destructive Raman HSI. Partial least squares regression (PLSR) techniques were used to develop the model using a calibration model based on 70% spectral data, and the remaining 30% of the data were used for validation. The results indicate that the Raman HSI, combined with PLSR, resulted in a protein and lipid model Rp2 of 0.90 and 0.82 with Root Mean Squared Error Prediction (RMSEP) 1.27 and 0.79, respectively. Additionally, this study successfully used the Raman HSI approach to create a prediction image showing the distribution of the targeted components, and could predict protein and lipid based on a single seeds.

Nondestructive Detecting Maturity of Pineapples Based on Visible and Near-Infrared Transmittance Spectroscopy Coupled with Machine Learning Methodologies

Pineapple is mainly grown in tropical regions and consumed fresh worldwide due to its attractive flavor and health benefits. With increasing global production and trade volume, there is an urgent need for nondestructive techniques for accurate and efficient detection of the internal quality of pineapples. Therefore, this study is dedicated to developing a nondestructive method for real-time determining the internal quality of pineapples by using VIS/NIR transmittance spectroscopy technique and machine learning methodologies. The VIS/NIR transmittance spectrums ranging in 400–1100 nm of total 195 pineapples were collected from a dynamic experimental platform. The maturity grade and soluble solids content (SSC) of individual pineapples were then measured as indicators of internal quality. The qualitative model for discriminating maturity grades of pineapple achieved a high accuracy of 90.8% by the PLSDA model for unknown samples. Meanwhile, the quantitative model for determining SSC also reached a determination coefficient (RP2) of 0.7596 and a root mean square error of prediction (RMSEP) of 0.7879 °Brix by the ANN-PLS model. Overall, high model performance demonstrated that using VIS/NIR transmittance spectroscopy technique coupled with machine learning methodologies could be a feasible method for nondestructive and real-time detection of the internal quality of pineapples.